System for knowledge acquisition

ABSTRACT

A system and method that translates sentences of natural language text into sets of axioms of formal logic that are consistent with parses resulting from NLP and acquired constraints as they accumulate. The system and method further present these axioms so as to facilitate further disambiguation of such sentences and produces axioms of formal logic suitable for processing by automated reasoning technologies, such as first-order or description logic suitable for processing by various reasoning algorithms, such as logic programs, inference engines, theorem provers, and rule-based systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of and claims priority under 35 U.S.C.§ 119(e) from U.S. patent application Ser. No. 14/889,004, entitled“System For Knowledge Acquisition” and filed on Nov. 4, 2015, which is aU.S. National Stage Application of PCT/US2014/037156 filed May 7, 2014,which claims priority to U.S. Provisional Patent Application No.61/820,350, entitled “A System For World-Wide Knowledge Acquisition” andfiled on May 7, 2013, the contents of which are incorporated herein byreference in their entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention pertains to systems and method for increasing thesemantic and logical precision of information extracted from naturallanguage text.

2. Description of the Related Art

Treebanks

Stand-off annotation is commonly used for annotating sentences withlexical and syntactic structure for use in the development of naturallanguage processing (NLP) systems. Repositories of such annotationtypically annotate words within sentences by part of speech andconstituents of sentences as parse trees where leaves of such trees arewords tagged with their part of speech and internal nodes of such treesare annotated with a classification of their syntactic function, such asany of various types of phrases or clauses. Consequently, suchrepositories of annotations are commonly called “treebanks”. The PennTreebank, for example, annotates words with one of several dozen part ofspeech tags, phrases with one of a couple dozen phrase tags, and clauseswith one of several clause tags.

Treebanks consist primarily of lexical and syntactic (i.e., word,phrase, clause) tags and syntactic structure (i.e., phrase and clausalstructure as in parse trees). Treebanks typically contain little or nosemantic annotation.

Treebanks are typically constructed by linguists who typically usesoftware tools to manually annotate natural language text with tags thatspecify lexical and syntactic details of fragments of the text withoutregard to any particular grammar or NLP system or technique.

Discriminatory Treebanking

In cases where a specific NLP technique is adopted for purposes oftreebanking, a technique known as discriminatory treebanking may beemployed. Discriminatory treebanking uses the output of an NLP systemwhich implements the adopted technique in formulating a set of choiceswhich discriminate between the set of parses obtained from the NLPsystem for any input fragment of text.

The set of discriminants presented as choices in discriminatorytreebanking may include any or all of the lexical, syntactic, andsemantic information produced by the NLP system. The number ofdiscriminants may be many times the number of words, phrases, or clausesin the input fragment of text, however. The number of lexicaldiscriminates increases with the size of the vocabulary and the numberof lexical features considered by the NLP system. The number ofsyntactic discriminants increases geometrically with the number oflexical discriminants as well as geometrically in the length of theinput fragment of text and with the breadth of coverage or number ofnon-terminals in the grammar used in the NLP system. The number ofsemantic discriminants increases in proportion to the number word sensesor ontology concepts and how they are related to the lexical orsyntactic terminals or non-terminals used in the NLP system.

Since the number of syntactic discriminants that would otherwise bepresented for disambiguation is combinatoric in the size of thevocabulary and coverage of the grammar, prior approaches todiscriminatory treebanking have been limited to small vocabularyapplications with narrow-coverage grammars or fine-grained linguistic orsemantic features with little or no discrimination among syntactic orsemantic structural alternatives, such as phrase or clausal ambiguities.

Each of the prior approaches to discriminatory treebanking falls shortof enabling large scale acquisition of knowledge from natural language.In the first case, limitations on vocabulary and grammatical coverageeffectively precludes the use of such prior discriminatory treebankingapproaches on uncontrolled natural language. In the second case,fine-grained features lacking discrimination among syntactic or semanticstructural alternatives (e.g., parse trees or logical atoms) limits thepotential user community to those educated in formal linguistics andfamiliar with the technical details of the underlying NLP system.Moreover, prior feature-based approaches commonly yield scores if nothundreds of discriminants which lack context due to their finegranularity, making discrimination a more error-prone process limited toexpert users who effectively search for an acceptably discriminatorypath through difficult choices among many alternatives.

Limitations of Discriminatory Treebanking

Discriminatory treebanking discriminates among the set of parsesprovided by an underlying NLP system with the objective of determiningthe correct interpretation of the input text. The process ofdiscriminatory disambiguation begins with obtaining the full set ofparses from the underlying NLP system. Unfortunately, for many naturaltexts, the ambiguity of sentences can produce hundreds or thousands ofparses when a large vocabulary, broad coverage grammar is used, asrequired for uncontrolled natural language text. In the case of certainNLP systems which are not constrained by a grammar, such as statisticaldependency parsers, the number of possible parsers is exorbitant.Consequently, in each case, a limit on the number of parses consideredis somewhat arbitrarily imposed. In the event that the correct parse isnot ranked or otherwise returned within such a limited result set,discriminatory treebanking will fail. In practice, this limit is setfairly low for various reasons, including the time and space required bythe NLP system and to process the results into discriminants and renderthem in the discriminatory user interface. Without such limits, thenumber of choices presented would be overwhelming in many cases andpossibly impractical in the case of NLP systems such as statisticaldependency parsers.

In the case of NLP systems that require lexical entries for words toexist in order to produce parses, unknown words or missing entries forcertain parts of speech or argument structure are another common causeof failure in discriminatory treebanking. The set of possible parseswill simply not contain parses for a word not previously known to havethe part of speech or valence required by such an NLP system in order toproduce the desired parse.

The parsers of most NLP systems are limited to accepting a total orderof tokens from the input text. In natural text, however, there isambiguity in how to segment text into tokens. Thus, another cause ofdiscriminatory failure arises when the input sequence of tokens is notas required in order to produce the desired parse. In such cases, thereis no discriminant for the overlooked token. Moreover, some approachesinvolve tagging tokens with a single part of speech before parsing. Whenthe assigned part of speech is not as required in order to produce thedesired parse, another case of discriminatory failure occurs.

Supporting the full-generality of a partially ordered lattice of tokens,each with possibly many parts of speech, increases the negative effectsof limiting the number of parses retrieved from NLP systems, increasesthe computational burdens of parsing such that on-line NLP becomesimpractical, and the number of discriminants becomes overwhelming forlarge vocabulary, broad coverage processing of natural language text.

Semantic Disambiguation

Treebanking tools are focused primarily upon annotating and, wheresupported by an NLP system, disambiguating the lexical and syntacticaspects of sentences. Treebanking tools largely ignore semantic andlogical precision.

Treebanking tools that are supported by an NLP system may go furthertowards annotating some of the semantic and logical aspects of sentencesbut their support for such aspects are limited to and by the underlyingNLP system. For example, the lexical annotations are limited to thelexical entries of the underlying NLP system. Similarly, the semanticaspects are limited to the semantically and logically under-specifiedrepresentations provided by some NLP systems, such as dependencies orminimal recursion semantics.

Underspecified Quantification

Parses resulting from NLP systems do not produce enough information todirectly obtain a formal semantics of English. In particular, most NLPsystems do not even consider quantification and few NLP systems handlequantification sufficiently for the purpose of obtaining axioms offormal logic from natural language.

Lexicalized grammars, in which a richer description of lexical entries(e.g., words) are processed by the underlying NLP system, typicallyproduce some semantic information, including underspecifiedquantification. Probabilistic context-free grammars resulting fromstatistical NLP produce phrase structure (i.e., parse) trees, but noinformation about logical quantifiers or their scope. Additionalprocessing of parse trees may provide dependency structures, as producedby dependency grammars. Dependency grammars typically provide little orno direct information concerning quantification, but may be furtherprocessed to produce some constraints on the relative scope ofquantifiers. In all cases, however, the resulting logic remainsunderspecified with regard to quantification, both in terms of thequantifier and its scope.

Quantifier Ambiguity

Most quantifications resulting from NLP are underspecified in severalaspects. From a classical logic the standpoint, the most significantambiguity is whether a quantification is existential or universal innature. There are many other aspects of quantification and it arises innatural language, however, and most of them are typically overlooked.One such aspect is whether the universal should be interpreted as beingimplicative with or without implied existential quantification of itsrestricting or binding formulation. Another aspect is whether thequantifier is first- or higher-order over individuals, types, or sets.Another aspect is whether the quantifier is singular or plural. Anotheraspect is polarity, such as negative with respect to universal orexistential. Another aspect is whether the quantifier is defeasible. Forexample, generalized quantifiers for determiners such as ‘most’ or‘typical’ may be treated as universal quantification in defeasiblelogic. Similarly, generalized quantifiers for ‘few’ or quantifiers forontological promiscuity introduced for adverbials or adjectives, such as‘rarely’ or ‘unusual’, may be treated as negations applied to defeasiblequantifiers, such as corresponding to the negation of a defeasibleexistential or universal.

Scope Ambiguity

Even after complete disambiguation, the logical ambiguity of mostsentences is extreme. Considering only noun phrases, for example, thereare as many as N factorial (N!) “readings” corresponding to the numberof permutations of generalized quantifiers introduced for each nounphrase. In practice, the number of linguistically plausible logicalreadings is smaller but nonetheless explosive. Logical reasoning withknowledge “contained” in text requires that the disjunction of readingsresulting from such NLP be effectively eliminated or that means ofreasoning more directly with underspecified representation be developed.

Noun phrases are not the only source of scope ambiguities, however.Additional ambiguities arise, for example, with regard to the implicitevents or situations of Davidsonian representation. Typically,Davidsonian representation of circumstances involves an implicitvariable for each verb and prepositional phrase, for example.Furthermore, ellipsis frequently results in arguments of valent lexemes(especially transitive verbs, but also including transitive adjectivesand nouns) being omitted from parses returned by NLP. Such elidedarguments, although missing from the grammatical analysis of NLP, aretypically required in axioms of formal logic using Davidsonianpredicates or certain semantic roles of neo-Davidsonian representation.Each of such arguments to predicates in the axioms of formal logic maycontribute an additional quantifier, thereby further exploding thelogical ambiguity of even fully disambiguated parses (e.g., the finalproducts of treebanking).

Reference Ambiguity

In under-specified semantics resulting from NLP, predicate-argumentstructures may involve arguments that logically refer to another, suchas in the case of pronouns, definite references, and other forms ofanaphora. In other cases, a predicate may logically require an argumentwhich does not occur within the text, such as omitting the subject of apassive verb and other forms of ellipsis. Either of ellipsis or anaphoramay require co-reference resolution, which may involve logical equalityor inequality.

Connective Ambiguities

Depending on the underlying NLP system, the logical semantics oflinguistic conjunctions, such as coordinating conjunctions, isfrequently not explicit in the parse information returned. That is, evenafter unambiguous treebanking, the logical semantics of a conjunctionmay remain logically or semantically ambiguous. Such ambiguity may bestructural (i.e., concerning which constituents belong to theconjunction) or semantic. Grammatical disambiguation typically resolvesmost structural ambiguity while typically leaving semantic ambiguitiesunresolved. For example, the scope of an ‘and’ may be clearly specifiedin a treebank, but whether the semantics is set theoretical union ordisjunction (i.e., collective or distributive) typically remainsunderspecified. Similarly, the scope of an ‘or’ may be clarified duringgrammatical disambiguation, but whether its semantics is inclusive orexclusive typically remains underspecified.

Even when the structural ambiguity of connectives is resolved, whether aquantifier is within the scope of a connective versus the connectivebeing within the scope of a quantifier typically remains unresolvedafter treebanking. The relative scope of quantifiers with regard toconnectives is particularly troublesome and important with regard tonegation (a degenerate, i.e., unary, logical connective).

Grammatical Complexity

General purpose, broad-coverage NLP systems use a variety of grammaticalformalisms and techniques to parse natural language. The simplestcontext-free grammars (CFG) model language with a small set ofnon-terminals corresponding to basic parts of speech and types ofphrases and clauses. The most basic parts of speech include nouns,verbs, adjectives, adverbs, conjunctions, prepositions, and articles.The most basic types of phrases include noun phrases, verb phrases, andprepositional phrases. And the most basic types of clauses includenon-finite clauses, relative clauses, and sentences. The parsesresulting from NLP using a CFG correspond to tree structures where theleaves are parts of speech classifying the input lexemes by part ofspeech, internal nodes correspond to phrases or clauses, and the rootnode corresponds to the input sentence.

CFG lack any semantic constraints, such as agreement between the person,number, gender, tense, aspect, mood and other semantics of naturallanguage and logic. Consequently, the parses resulting from NLP usingCFG include parses that make no sense, semantically or logicallyspeaking. As a result, many NLP systems eschew or extend CFG in order toavoid results that make no sense. Alternatives to CFG include variousformalisms, most of which may be classified as being dependency- orconstituency-based formalisms.

Constituency-based formalisms typically produce parses in which asentence comprises constituent phrases or clauses which typicallycomprise lexemes, phrases, or clauses. Sentence structures astraditionally depicted in elementary school are constituency-based inthat each node spans part of the sentence without gaps. Dependency-basedformalisms typically produce parses in which parts of the sentence arerelated to one another but not necessarily in a way that corresponds toa span of the sentence. Constituency-based formalisms tend to begrammars crafted by linguists while dependency grammars are common instatistical NLP systems. Constituency-based formalisms have been thebasis for prior approaches to discriminatory treebanking.

CFG is a constituency-based formalism that lacks semantic constraints,as discussed above. A common technique to extend CFG is to “lexicalize”the grammar such that lexical entries for words are described with moresemantics and the production rules of CFG are augmented with constraintsbetween the non-terminals which occur in such rules that are “unified”during parsing. Such lexicalized or unification grammars and processingmechanisms include lexical function grammar, head-driven phrasestructure grammar (HPSG), and combinatory categorical grammar (CCG).Parses which result from such grammars include semantic informationpropagated through unification from lexical entries through theproduction rules of the grammar to the resulting constituents of suchparses. In some such NLP systems, the unification of lexical entries andgrammar rules augment parse results with predicate-argument structurescorresponding to parts of formulas in formal logic. In one family ofHPSG systems, the predicate-argument structures resulting from NLP use aformalism known as “minimal-recursion semantics”. In another family ofCCG systems, the predicate-argument structures resulting from NLP use aformalism known as “hole semantics”. Such predicate-argument structureformalisms are known as under-specified semantic representations. Suchrepresentations are under-specified in that they do not resolve thelogical quantification of variables or any equalities or inequalitiesbetween such variables occurring in their predicate-argument structures(as discussed above with regard to generalized quantifiers, scope,anaphora, and co-reference resolution).

Lexicalized dependency grammars are prevalent in machine learningapproaches to NLP. Unlike constituency grammars, which are typicallycrafted by linguists, machine learning approaches to NLP generallyinduce a probabilistic context-free grammar or dependency grammar givena vocabulary of lexical entries and, in many cases, a database of “goldparses” as in a treebank. Parses resulting from dependency-based NLPsystems lack the non-terminal labels corresponding to production rulesof a constituency-based grammar. The results of such NLP formalisms lackthe predicate-argument structures discussed above, although variousalgorithms have been developed to produce constituency andpredicate-argument structures from the results of dependency-based NLP.

SUMMARY OF THE INVENTION

The present invention is directed towards incrementally increasing thesemantic and logical precision of information extracted from naturallanguage. The invention picks up where traditional approaches to lexicaland syntactic disambiguation leave off. It applies state of the artmethods to the traditional task of determining the correct grammaticalparse from fragments of text (most typically sentences) in an integratedprocess of semantic and logical disambiguation so as to produce axiomsof formal logic which may be as precise as in classical logicformalisms, such as first-order predicate calculus, or less fullyspecified axioms in which the scope or nature of quantification isunder-specified, for example. The resulting axioms allow more preciseinference from text along of spectrum of variable precision semanticsfrom more uncertain information extraction thru heuristic reasoning bytextual entailment to robust logical reasoning using classical ordefeasible logic formalisms.

The present invention is directed to a system for reducing the ambiguityof natural language text so as to increase the precision of its logicalsemantics for processing by computers and translate sentences fromnatural language into axioms of formal logic. As such the presentinvention assists in constraining the logical semantics of text anddetermines axioms of formal logic which are consistent with sentences ofsuch text given any such constraints.

The present invention clarifies the logical semantics of text byfacilitating the acquisition and management of constraints upon certain(e.g., lexical, syntactic, semantic, or logical) aspects of andrelationships between constituents of text (e.g., words, phrases,clauses, or sentences) so as to increase the precision of informationretrieval, natural language processing, and automated reasoning systemswith regard to said text and knowledge expressed therein. The presentinvention facilitates incremental and collaborative accumulation andcuration of such constraints which reduce and may effectively eliminatelexical, syntactic, semantic or logical ambiguities of sentences withinsaid text. The present invention further facilitates acquisition of suchconstraints using natural language processing (NLP) incorporatingfeedback of accumulating constraints. The present invention also furtherfacilitates disambiguation by determining and presenting logical axiomsthat are consistent with accumulating constraints and parses resultingfrom NLP that are similarly consistent.

The present invention translates sentences of natural language text intosets of axioms of formal logic that are consistent with parses resultingfrom NLP and acquired constraints as they accumulate. The presentinvention further presents these axioms so as to facilitate furtherdisambiguation of such sentences and produces axioms of formal logicsuitable for processing by automated reasoning technologies, such asfirst-order or description logic suitable for processing by variousreasoning algorithms, such as logic programs, inference engines, theoremprovers, and rule-based systems.

In one embodiment, a method for reducing the logical ambiguity of aportion of natural language text is provided. The method includesalgorithmically interpreting the portion of natural language text in acomputer system into a number of data structures representing one ormore interpretations of the portion of natural language text as a numberof logical formulas. Each of the data structures include: (i) a numberof first data structures representing formulas pertaining to or betweenreferents within any of the interpretations, wherein each of the firstdata structures represents a predicate using as an argument a variablecorresponding to a thing or a situation referenced explicitly orimplicitly within the interpretations; (iii) a number of second datastructures representing formulas of logical quantification eachpertaining to a variable used as an argument in a number of the logicalformulas and wherein each logical formula represented by a second datastructure has or should have scope over all other logical formulas whichuse the variable to which the logical formula represented by the seconddata structure pertains; and (iii) a number of third data structureseach representing a constraint on the relative scopes of a respectivepair of formulas, wherein each of the third data structures indicateswhether one formula of the respective pair of the formulas occurs withinthe scope of the other formula of the respective pair of the formulas.The method further includes, in a user interface, enabling specifyingwhether a first particular one of the logical formulas occurs within ascope of a second particular one of the logical formulas so as toaugment the constraints represented by the third data structures andreduce the logical ambiguity among the interpretations of the portion ofnatural language text.

In another embodiment, a method for facilitating interpretation of aportion of natural language text is provided. The method includes: (a)algorithmically interpreting the portion of natural language text by acomputer system into a number of partial parses and zero or more fullparses, (b) algorithmically deriving a set of discriminants where eachdiscriminant corresponds to a number of the partial parses, (c)algorithmically determining whether or not each of the partial parses isconsistent or inconsistent with a subset of the discriminants whereineach discriminant in the subset specifies whether it is consistent witha correct interpretation of the text, (d) algorithmically determiningwhether each of the discriminants: (i) is consistent with any of thepartial parses which are not inconsistent with a correct interpretationof the portion of natural language text; or (ii) is inconsistent withany of the partial parses which are consistent with a correctinterpretation of the text; and (e) enabling a user interface forspecifying whether or not each of a number of the discriminants isconsistent with a correct interpretation of the text.

In still another embodiment, a method for facilitating interpretation ofa portion of natural language text. The method includes: (a)algorithmically interpreting the portion of natural language text by acomputer system into a number parses, (b) algorithmically deriving a setof discriminants from the parses, (c) algorithmically determiningwhether or not each of the parses is consistent or inconsistent with thesubset of the discriminants wherein each discriminant in the subsetspecifies whether it is consistent with a correct interpretation of saidtext, (d) algorithmically determining whether each of the discriminants:is consistent with any of the parses which are not inconsistent with acorrect interpretation of said portion of natural language text; or isinconsistent with any of the parses which are consistent with a correctinterpretation of said text, (e) enabling a user interface forspecifying whether or not each of a number of the discriminants isconsistent with a correct interpretation of said text, and (f) enablingin the user interface repeated algorithmic interpretation of saidportion of natural language text constrained by any discriminantsspecified as consistent or inconsistent in furtherance of obtainingcorrect interpretations.

In yet another embodiment, a method for reducing a number of predicatesin an original formula and generating a resulting formula is providedthat includes generating one formula in the resulting formula byconflating two formulas in the original formula where a predicate in theresulting formula is composed from a predicate of one of the twoformulas in the original formula which modifies a predicate of the otherof the two original formulas in the original formula, and displaying theresulting formula on a user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a preferred embodiment of a knowledge acquisitionprocess in the present invention.

FIG. 2 is a diagram of a preferred embodiment of a collaborativearchitecture in the present invention.

FIG. 3 is a diagram of a preferred embodiment of a disambiguationprocess in the present invention.

FIG. 4 is a diagram of the preferred embodiment of discriminatorytreebanking in the present invention.

FIG. 5 is a computer screen shot of a preferred embodiment of a userinterface for specifying parts of speech for unrecognized tokens, suchas words, in accordance with the system of the present invention.

FIG. 6 is a computer screen shot of a preferred embodiment fordiscriminatory disambiguation in accordance with the system of thepresent invention.

FIG. 7 is a computer screen shot of a preferred embodiment of thedisambiguation user interface pertaining to logical disambiguation of aselected full parse or an unambiguous sentence of the system of thepresent invention.

FIG. 8 is a computer screen shot of a preferred embodiment showingresults of logical disambiguation after specification of quantifiers andan equality and selection of a reading in accordance with the system ofthe present invention.

FIG. 9 is a computer screen shot of a user interface for logicaldisambiguation of readings consistent with a grammatically unambiguousparse in a preferred embodiment of the system of the present invention.

FIG. 10 is a computer screen shot of a user interface for logicaldisambiguation of readings consistent with a grammatically unambiguousparse after simplification by suppression of “optional” variables in apreferred embodiment of the system of the present invention.

FIG. 11 is a diagram of a process by which formulas are conflated in apreferred embodiment in accordance with the system of the presentinvention.

FIG. 12 is a diagram of a process for disambiguating logic in apreferred embodiment in accordance with the system of the presentinvention.

FIG. 13 is a diagram of a preferred embodiment of the integration of thedisambiguation and natural language processing subsystems of the presentinvention.

FIG. 14 is a computer screen shot of a preferred embodiment forgrammatical disambiguation focused on semantics in accordance with thepresent invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As used herein, the singular form of “a”, “an”, and “the” include pluralreferences unless the context clearly dictates otherwise.

As used herein, the statement that two or more parts or elements are“coupled” shall mean that the parts are joined or operate togethereither directly or indirectly, i.e., through one or more intermediateparts or elements, so long as a link occurs.

As used herein, “directly coupled” means that two elements are directlyin contact with each other.

As used herein, “fixedly coupled” or “fixed” means that two elements arecoupled so as to move as one while maintaining a constant orientationrelative to each other.

As used herein, the word “unitary” means a part is created as a singlepiece or unit. That is, a part that includes pieces that are createdseparately and then coupled together as a unit is not a “unitary” partor body.

As employed herein, the statement that two or more parts or elements“engage” one another shall mean that the parts exert a force against oneanother either directly or through one or more intermediate parts orelements.

As employed herein, the term “number” shall mean one or an integergreater than one (i.e., a plurality).

As used herein, the terms “component” and “system” are intended to referto a computer related entity, either hardware, a combination of hardwareand software, software, or software in execution. For example, acomponent can be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and/or a computer. By way of illustration, both anapplication running on a server and the server can be a component. Oneor more components can reside within a process and/or thread ofexecution, and a component can be localized on one computer and/ordistributed between two or more computers. While certain ways ofdisplaying information to users are shown and described with respect tocertain Figures or graphs as screenshots, those skilled in the relevantart will recognize that various other alternatives can be employed. Theterms “screen,” “web page,” and “page” are generally usedinterchangeably herein. The pages or screens are stored and/ortransmitted as display descriptions, as graphical user interfaces, or byother methods of depicting information on a screen (whether personalcomputer, PDA, mobile telephone, or other suitable device, for example)where the layout and information or content to be displayed on the pageis stored in memory, database, or another storage facility.

Directional phrases used herein, such as, for example and withoutlimitation, top, bottom, left, right, upper, lower, front, back, andderivatives thereof, relate to the orientation of the elements shown inthe drawings and are not limiting upon the claims unless expresslyrecited therein.

As shown in FIG. 1, a preferred embodiment of the present inventionincorporates a web browser through which web search can be used to findcontent to be annotated with linguistic, semantic, and logicalinformation. The integrated web browser allows for existing web contentto be copied into a library of web pages, as in a wiki, whichconstitutes the web site to be annotated. Any such web content may beprocessed through an HTML to XML converter and edited in an integratedXHTML tool. The XHTML tool renders the document object model (DOM) ofthe XML conformant HTML as a navigable tree structure and renders thecontent in text and HTML views. Positioning the cursor in the text orHTML expands the DOM tree to expose the XML element containing thecursor and any selected text. An edit operation creates a new XMLelement within the DOM using a tag selected from an ontology ofconcepts. The type, attributes, or content of existing XML elements inthe DOM may be also edited. In particular, sentences may be selectedwithin web pages and marked as such using XML elements. Furthermore,individual tokens (e.g., words) and sequences of tokens (e.g., phrasesor clauses) may be similarly tagged using this facility.

Any XML element corresponding to a sentence may be annotated anddisambiguated as described below using this mechanism. The result ofannotation and disambiguation is represented as XML which furtherannotates the tokens, words, phrases, clauses, and sentence as would beannotated using standard annotation tools used in non-discriminatorytreebanking.

The set of such sentences annotated within the XHTML content of the website are presently stored as independent XML documents on a knowledgebase server. The client application that incorporates the web browserincludes panes displaying all such sentences in addition to the panesfor the web browser and the XHTML annotation tool. Sentences notresiding within web pages may also be added to the knowledge base byclients.

As shown in FIG. 2, any sentences annotated or added to the knowledgebase by one client are made visible to other active clients in thesentences pane. The clients and server collaborate in maintainingprovenance information. Clients authenticate with the server and theserver records who first adds a sentence to the knowledge base and eachchange by any other annotator. The time, type and operands of eachoperation performed is also recorded along with the user of the client.This information is propagated to active clients such that the sentencespane provides real time visibility to the collective activity ofannotators.

As shown in FIG. 3, when a client commences annotation of a sentence forthe first time, an underlying NLP system is invoked so as to provide anumber of parses which inform annotation and facilitate disambiguationusing the discrimination approach described further below. Although NLPis not strictly required in order to annotate a sentence, the typicaland supported workflow is to do so. The results of NLP are presented inone or more tabs within the client application. The tabs provide variousperspectives and interactive functionality to facilitatediscrimination-based annotation. Upon exiting the client or closing ananalysis tab any disambiguation is stored on the server as constraintswithin an XML document. Partial work with little or no disambiguationactivity may be saved, as in loading a knowledge base with a number ofinitial sentences without manual intervention or in the coordination ofwork between collaborators who are more focused on grammaticaldisambiguation versus collaborators who are more skilled at logicaldisambiguation. In the latter case, the results of partial grammaticaldisambiguation or completion of grammatical disambiguation to the pointof identifying an individual parse may be saved whereupon it becomesvisible to others with a status of being unambiguous but non-axiomatic,as discussed below.

The XML document for any given sentence in the knowledge base reflectsthe status of disambiguation of that sentence. Ideally, a sentence willbe axiomatic, in that it will have been disambiguated to the point thata small number of axioms of formal logic have been identified (typicallyone). Sentences may remain grammatically ambiguous. Grammaticallyunambiguous sentences are typically either axiomatic or they arelogically ambiguous or contain unresolved anaphora, such as pronouns, orellipsis. A sentence that is grammatically unambiguous but not axiomaticis the end result of prior approaches to treebanking (i.e., logical andsemantic ambiguities remain after grammatical disambiguation).

The status of sentences in the knowledge base (including sentenceswithin pages of the web site) are plainly visible to clients and updatedin real-time as collaborative curation of the web site and knowledgebase proceeds. Various views of extensive information concerningsentences, including their status, provenance, annotation history,recency of various activities, relationships to other sentences,ratings, comments, and axioms are provided with extensive sorting andfiltering capabilities. Thus, the architecture and user interface of thepresent invention facilitate the collective workflow and collaborationamong clients.

The system incorporates logic generation capabilities which translateaxiomatic sentences into axioms of formal logic in the syntax requiredby any of various reasoning systems. In particular, the system generatesaxioms in classical logical syntax as supported by, for example,first-order logic systems (The initial reasoning system targeted isVulcan's SILK system (The SILK System: Scalable and Expressive SemanticRules, 2009)). Further, the system analyzes the disambiguated sentencesof a knowledge base so as to generate logical axioms that supportlogical reasoning given the terminology used in the axioms generateddirectly from axiomatic sentences. These additional axioms comprise anontology relating the terms (including predicates) used in the axiomsgenerated from axiomatic sentences.

Combined with a lexical ontology incorporated within the presentinvention, the generated ontological axioms support reasoning using theaxioms generated from axiomatic sentences so as to solve problems suchas in answering questions.

Overview of Disambiguation Process

As shown in FIGS. 4 and 13, the raw text of a sentence to bedisambiguated is processed into a partially ordered lattice of tokenswhich are further processed into a partially ordered lattice of lexemesas input to a chart parser. The tokens lattices is not limited to atotally order sequence as is typical in NLP systems and prior approachesto treebanking, however. This avoids failures arising from tokenizationmistakes. One or more tokens along a path through the token lattice arefurther recognized as lexemes or multi-word expressions, such as words,numbers, dates, times, and other varieties of lexical types commonlyoccurring in text. For lexemes that are words, parts of speech andlexical features, such as person, number, gender, tense, aspect, mood,and other features which vary with the underlying NLP systems furtherannotate such lexemes and all lexemes are used as the initial state forparsing using any of various NLP systems. In the case of chart parsers,as are common in statistical, dependency, and grammar-based NLP systems,the lattice of lexemes populates the chart initially whereupon theparsing algorithms repetitively analyze and augment the state of thechart, typically until finding one or more parses which span the input,failing to find any parse which spans the input, or exceeding time orspace limitations. The state of the chart when the parses pauses is thenavailable for use in disambiguation.

The preceding describes a preferred embodiment of the present inventionin which the underlying NLP system supports a lattice of tokens asinput. In some such systems, the token lattice may be the initial inputto the chart and the parsing algorithm may derive the lexeme latticeusing algorithms or data structures, such as lexical entries whennatural language processing is based on a lexicalized grammar, such ashead-driven phrase structure grammar (HPSG) or combinatory categoricalgrammar (CCG), for example. In less preferred embodiments a tokendisambiguation step may be introduced between morphological analysis andsubsequent lexical and syntactic processing or as remediation ifdisambiguation fails to determine an acceptable parse given incorrecttokenization assumptions.

Similarly, the preceding describes a preferred embodiment of the presentinvention in which the underlying NLP system is not limited to a totallyordered sequence of lexemes each tagged with a single part of speech. Insuch less preferred embodiments, a part of speech disambiguation stepmay be introduced after or in combination with token disambiguationbefore subsequent syntactic processing or as remediation ifdisambiguation fails to determine an acceptable parse given incorrectpart of speech tagging assumptions.

In a preferred embodiment, the underlying NLP system should be capableof performing lattice tokenization and part of speech tagging given theraw text so as to consider all possible tokens and lexemes which mightpossibly yield one or more partial parses. In a further preferredembodiment, the present invention has access to token and lexemelattices as well as the partial and full parses residing in the chartwhen NLP pauses. In the event that an underlying system is limited ineither regard, the present invention may augment its tokenization andpart of speech tagging capabilities with auxiliary information andprocessing, such as regular expression pattern matching for latticetokenization recognition of abbreviated, misspelled, hyphenated andother morphological variations in lexemes. Moreover, in the presentinvention, such auxiliary morphological processing may normalizecharacters, including punctuation and whitespace, such as variousUNICODE alternatives for equivalent strings or characters not otherwisehandled or recognized as possibly equivalent by an NLP system.

In the preferred embodiment, the foregoing process quiesces with thechart populated with a lattice of tokens, lexemes, and partial and fullparses. In some cases there may be a gap where no lexeme covers aninterval of the input, such as in the case of an unrecognized or unknownword. In such cases, there will be no full parses. As depicted in FIG.4, the present invention handles lexical gaps and the absence of a fullparse. In the case of lexical gaps, the present invention may prompt forclarification as to the plausible parts of speech for tokens adjoiningor spanning such gaps, such as shown in FIG. 5. In a preferredembodiment, the system augments the state of the chart with lexemescorresponding to possibly multiple plausible parts of speech. In theevent the underlying NLP system is limited to a single part of speechfor any such token or span, the system would preclude multiple selectionin the dialog shown in FIG. 5.

In a preferred embodiment, the system updates the lexeme lattice as itresides in the chart directly and resumes syntactic processing untilquiescence. Alternatively, the system repeats the NLP pipeline, albeitwith the constraints acquired thus far during discrimination and fromuser clarification as to lexical gaps. In the present invention, suchmore informed and constrained NLP is realized by providing the NLP witha token and lexeme lattice that are limited to the tokens and lexemesthat are consistent with user input recorded for all priordisambiguation (including the current and prior sessions, unless suchdisambiguation inputs have been “reset” or cleared, as discussed below).In addition to constraining NLP morphologically and lexically, thesystem can constrain syntactic and semantic processing, as discussedbelow.

NLP may quiesce with no full parse spanning the input for severalreasons other than lexical gaps. In one such cases, the input may not berecognizable given the grammar (or statistical algorithms) of theunderlying NLP system. In another common case, the ambiguity of theinput may require more time or space than NLP is permitted. Such casesare more common in the case of large vocabulary, broad coverage grammarsapplied to long sentences, such as are typical when acquiring knowledgefrom textbooks, for example.

As depicted in FIG. 13, the present invention improves productivity andavoids many failures due to ambiguity by performing NLP iteratively withdisambiguation. In a preferred embodiment, for example, the presentinvention extracts the token, lexeme, and partial parse lattice from thechart and performs disambiguation without full parses as required byprior approaches. As the token, lexeme, and partial parse latticesbecomes more constrained through disambiguation, NLP may be resumed andproceed with increasing focus. In a preferred embodiment, for example,the present invention sets low limits on the time, space, and number ofparses to be computed by NLP. In the event that no full parses areobtained within the allotted resources, discrimination can proceed andNLP be resumed with greater focus, with or without increasing allottedtime or space. In the event that the specified number of parses areobtained, discrimination can proceed as normal and be followed byrepeatedly resumed NLP until disambiguation is completed. Mosttypically, such disambiguation is concluded only after NLP produces lessthe limited number of parses specified within allocated resources withno ambiguity remaining.

In a preferred embodiment, the present invention directly updates thechart of the underlying NLP system by removing any tokens, lexemes, orpartial parses that are inconsistent with the state of disambiguation.If a token is selected during disambiguation, all overlapping tokens areremoved from the chart. If a token is vetoed during disambiguation, itis removed from the chart. When a token is removed from the chart anylexemes that depend on it are similarly removed from the chart. When alexeme is selected during disambiguation, its constituent tokens arealso selected, any conflicting tokens are removed as discussedpreviously, and any overlapping lexemes are removed from the chart. Whena lexeme is removed from the chart, any partial or full parses that useit are similarly removed from the chart. When a partial or full parse isselected during disambiguation, the lexemes or partial parses on whichit depends are selected, thereby eliminating conflicting alternativesfrom the chart as discussed here, and any overlapping but uncontainedparse is removed from the chart. All this computation is in order tofocus NLP and is in addition to the standard computations ofdiscriminatory treebanking.

In other preferred embodiments, where direct access to the chart of theunderlying NLP system is not practical, the present invention may belimited to focusing NLP morphologically and lexically as discussedabove. In one such preferred embodiment, the specific lexical entrycorresponding to a particular syntactic valence and coarse semantics ofa word may be specified so as to constrain partial parses accordingly.In another such preferred embodiment, the intervals implied bydiscrimination may be used to filter the parses that are efficientlyenumerated best-first by various dynamic programming algorithms commonlyused in underlying NLP systems. In this embodiment, a result enumeratedfrom NLP will be filtered if it overlaps without subsuming the intervalof any token, lexeme, or parse implied by discrimination thus far.

In the present invention, the state of the chart residing in theunderlying NLP system may be reflected in real-time on a user interfacethrough which discriminatory disambiguation may proceed concurrentlywith NLP. In a preferred embodiment, disambiguation and NLP arephysically disconnected but logically interleaved. In said embodiment,the present invention communicates the results of NLP for disambiguationwhich communicates constraints for further NLP. The information conveyedfrom NLP to disambiguation consists of the constituency structure offull parses or the contents of the chart of input and intermediateresults. The information conveyed from disambiguation to NLP includesany revision of the input sentence, possibly including a lattice oftokens or lexemes, or updates concerning the selection or refutation ofchart items arising during disambiguation. In the present invention,such communication is in the form an XML document communicated over theInternet between a web service and a client application which includesthe user interface by which disambiguation is performed.

At any point the XML documents communicated between the NLP service andthe disambiguation user interface may be stored on a server alsoaccessed over the Internet by the client application. Such a server actsas the repository for all sentences subjected to or to be subjected tosuch NLP, disambiguation, and knowledge acquisition activity. Such arepository of sentences may be called a “knowledge base”. In the case ofthe present invention, documents may also persist in a serverrepository, such as web pages from which sentences residing in such aknowledge base originated or are otherwise related so as to be linked toor render information obtained through disambiguation, such as lexical,syntactic, semantic, and logical annotations of such sentences arisingfrom such disambiguation. Such a repository of documents may be called a“library”. The whole of the document and sentence repositories may alsobe called a knowledge base and rendered as web pages, such as on a wikiwith linguistic, semantic, and logical annotations and supporting aclient application for annotation via disambiguation as described here.

Each time a client adds a sentence to the repository and accesses asentence in the repository the authenticated user and the activity andoperations performed using the disambiguation user interface arerecorded within the sentence's XML document as it persists in therepository on the server. If the sentence originates from a document,such as a web page accessed using a browser integrated within the clientapplication, the source of said sentence is also recorded in itspersistent XML document. Consequentially, the XML document for asentence includes provenance and version control information along withan event log.

The initial state of a sentence's XML document in the repository may beas simple as the text of the sentence but typically includes its initialsource and author along with when it was created. The state of asentence which has undergone NLP includes additional information such asits length, number of parses, and status with regard to ambiguity. Thedocument may include information corresponding to the quiescent state ofNLP or omit such information if no disambiguation has yet occurred. Thedocument includes complete information concerning disambiguationinformation added by users, including which elements results from NLPhave been affirmed or refuted during disambiguation. An event logincludes who performed each such affirmation or refutation when,including whether prior disambiguation activity was cleared or undoneand whether NLP was repeated with or without editing the text of thesentence. In the event that the text of the sentence is modified, anevent logs who and when the edit was performed along with prior versioninformation of the sentence before such an edit.

The current state of disambiguation for any sentence within theknowledge base resides within its XML document. Such state ranges fromunparsed to grammatically disambiguated and further to logicallydisambiguated. Parsing an unparsed sentence results in an “incoherent”sentence if no full parses were determined by NLP. The status of asentence may also be incoherent if no full parse resulting from NLP hassurvived prior disambiguation. The status of a sentence is “ambiguous”if more than one full parse has resulted from NLP and survives any priordisambiguation. If only a single full parse results from NLP or survivesprior disambiguation, the status of the sentence is “unambiguous”. Thestatus of an unambiguous sentence becomes “axiomatic” if ambiguities ofquantification and reference have been resolved.

Upon receipt of an XML document from the knowledge base or NLP forpresentation in its disambiguation user interface, the clientapplication renders any or all of lexical gaps (as discussed above),discriminants, or logical information. If the sentence is ambiguous theclient application focuses on a presentation of discriminants as shownin FIG. 6. In its default configuration, the user interface presentslogical information if the sentence is unambiguous (including axiomatic)or when the user focuses on an individual full parse, as shown in FIG. 7in which the middle one of three parses shown is selected.

In FIG. 6, the “clauses” tab combines lexical, syntactic and semanticdiscriminants in accordance with the user's selection of “detailed” inthe combo-box on the toolbar to the upper right. FIG. 6 includes a“derivations” tab showing parses returned by NLP or that have surviveddisambiguation thus far as shown in FIG. 7. FIG. 6 also includes a“lexemes” tab indicating that there is lexical ambiguity remaining. Theicons on the toolbar are in their default configuration. The defaultconfiguration does not show any discriminant that occurs in allsurviving full parses (i.e., which does further discriminatorydisambiguation). Unlike prior approaches which assume exhaustive parsingprior to discrimination, the client application allows discriminantswhich do not participate in any full parse to be presented (such as tofurther iterate with parsing). By default, the client application doesnot show discriminants which have been affirmed or refuted until thesentence is unambiguous, at which point the client application focuseson the derivations tab on which logical information is presented as inFIG. 9.

When the client application receives an XML document from the knowledgebase or NLP for an ambiguous sentence it extracts the tokens, lexemes,and partial parses with default focus on those that participate in fullparses that survive discrimination thus far. If the document contains nofull parses, the contents of the chart that are consistent with anydiscrimination thus far are considered, unlike prior approaches todiscriminatory disambiguation. For each such constituent of anysurviving full parse or the chart, the application presents a clauseabstracting the details of the constituent. Such rendered clauses areabstractions in that they omit or generalize details resulting from NLPso that they are accessible to non-linguists. FIG. 6 shows lexical,syntactic, and semantic clauses which result from such processing. FIG.14 shows a subset of the clauses in FIG. 6 which are more semantic, suchas relating concepts designated by various tokens and their lemmas(which appear as terms in the predicates of various discriminantsrendered therein).

Constituents resulting from an NLP system typically include thenon-terminal of the production rule in its grammar that produced theconstituent. The number of distinct non-terminals in broad-coveragegrammars typically ranges in the hundreds of morphological and syntacticrules plus tens of thousands for the lexical entries of a largevocabulary NLP system. The variety of labels for such non-terminals isoverwhelming, even for linguists. Consequently, the present inventionincorporates an ontology of constituent types, roughly corresponding tothe basic parts of speech, the non-terminals of a simple CFG withadditional categories for various morphological and lexical rules whichhandle phenomena such as inflection and affixation. In adapting thepresent invention to an underlying NLP system, algorithms or datastructures are defined which classify the labels that may result fromNLP into said ontology.

Generally, tokens correspond to intervals of the input text. Tokens maybe provided to NLP or computed by an underlying NLP system. Lexemes mayalso be provided or computed by the NLP system. Lexemes correspond to asequence of tokens occurring in the possibly augmented input lattice oftokens along with one or more morphological or lexical rules thatrecognize how a lexeme may be derived from the stem of its lemma. Theamount of information available with a lexeme from an NLP system varieswidely but may be quite detailed in the case of lexicalized grammars.

The linguistic ontology of the present invention introduced aboveincludes the following attributes or categories concerning lexemes:

1. number: plural, singular

2. person: first, second, third

3. gender: masculine, feminine, non-neuter, neuter

4. mood: indicative, subjunctive

5. tense: past, future, un-tensed, tensed

6. form: declarative, interrogative, imperative, non-imperative

7. individuated

8. perfected

9. progressive

In a preferred embodiment of the system of the present invention, anysuch information concerning a lexeme within the chart of an underlyingNLP system is taken together with the lemma of said lexeme and thetoken(s) of said lemma to construct discriminants. Such discriminantsare rendered as unary or binary predicates in which the first argumentis the lemma or concatenated tokens. The present invention renders somepredicates as unary and others as binary based on user experience. Forexample, “number(‘his’,singular)” vs. “singular(‘his’). In other cases,the representation is a binary predicate in all cases, such as“perfected(‘running’,true)” vs. “perfected(‘running’,false)” where thelatter results only if the results of NLP are explicit regarding suchpredicates.

In a preferred embodiment of the system of the present invention, partof speech categories are composed with the lemma and lexeme occurring inNLP results to produce a discriminant such as “nominal(end)(‘ends’)” or“verbal(end)(‘ends’)” as shown in FIG. 14. Of course, a wide variety ofsuch renderings are possible. It should be noted, however, that theremay be multiple lexical entries for homographic words and senses of aword that vary in their valence or semantic constraints as processed,for example, by a unification grammar. In a preferred embodiment, thepresent invention produces one discriminant for a lexical entry of agiven part of speech even if there are multiple lexical entries withsimilar valence. In the sentence of FIG. 5, for example, there are twovalences of the noun ‘ends’ and one for ‘cell’.

Note that information about the position of a lexeme within the input isnot presented in the discriminant above. Thus, if a lexeme occurs morethan once within a sentence, each will correspond to the samediscriminant. For example, in the sentence “a cell has a nucleus”, thelexical discriminant for ‘a’ will be shared by each occurrence. Forexample, there are 7 occurrences of 3 words which occur more than oncein the sentence shown in FIGS. 5 and 6.

Avoiding details concerning lexeme position and lexical entries beyondthe coarse categories enumerated above reduces cognitive load duringdiscrimination without sacrificing discriminatory efficacy in practice,principally because the syntactic and semantic relationships whichdepend on lexical disambiguation are sufficient for such disambiguation.Doing so also reduces the need for linguistic skill and familiarity withdetails of underlying NLP systems during disambiguation.

Also note that avoiding such details in discriminants leads tocompounded reduction in the number of discriminants and the cognitiveload during discrimination. For example, in the sentence of FIG. 5, thelexical features for singular and possessive with regard to eachoccurrence of ‘cell’ are only needed once. More dramatic reduction ofthe number of discriminants is possible for syntactic discriminants.

In a preferred embodiment of the system of the present invention, thenon-terminals of the syntactic production rules of an underlying NLPsystem are abstracted away to basic categories such as nominal, verbal,prepositional, and adjectival or adverbial “bar constructions” orphrases (e.g., N, V, P, A spanning more than one lexeme and NP, VP, PP,AP, some of which appear in FIG. 6). Other non-terminals subsumingvarious types of clauses other syntactic constructions are typicallysuppressed to avoid their cognitive load and bias towards linguisticexpertise. In practice, omitting certain constituents does not interferewith discrimination because the basics of syntax are retained andaugmented by semantics, as discussed further below.

Just as the text of an input sentence may have many full parses, somight a span of the input correspond to multiple syntactic non-terminalsof the same type. In FIG. 7, for example, the 3 remaining parses end indifferent verb phrase structures for the same span of the sentence. Asshown in FIGS. 5 and 7, syntactic constructions involving more than onelexeme are rendered according to their tree structure traversed in orderwith an open parenthesis on upward and forward (rightward) transitionsand a closing parenthesis on upward and backward (leftward) transitionswhen viewing the parse as tree rooted at the top and read left to right.Within the rendering, the tokens of individual lexemes are enclosedwithin single quotes. Alternatively, such discriminants could berendered without quotes, substituting a space for opening parenthesesand dropping closing parentheses, as in rendering a span of the input.Discriminants rendered with their structure reflected as in the formerrendering convey more information but such rendering is not required.When such rendering is used, however, more information is gained fromeach discriminatory action. As discussed above concerning theintegration of disambiguation with chart parsers, selecting aconstituent comprising several lexemes precludes all other constituentswhich do not span or are not spanned by the selected constituent. Whendiscriminants are rendered with their internal structure, selectionsfurther eliminate any multi-lexeme or -token constituent within the spanwhich is not consistent with the structure of the selected constituent.

In the case of an underlying NLP systems based on dependency-grammar,the techniques of producing discriminants for tokens and lexemesdiscussed above apply, as do those for syntactic constituents, includingthe optional rendering of internal structure, such as using parentheses.

As discussed in the background information on NLP, some lexicalizedunification grammars produce predicate-argument structures in the courseof parsing. Such predicate argument structures correspond primarily toatomic formulas as in classical logic. Each such atomic formulacomprises a predicate with a number of arguments. Certain grammaticalrules introduce other information that do not logically correspond topredicates or arguments as much as to non-atomic formulas, such asquantifying, modal, or negative formulations which d which have scopeover other formulas. For example, the word “no” may indicate aquantifying formulation when applied in a noun phrase or a negativeformulation when applied to a verb phrase. Where practical, thelinguists who author lexical unification grammars augment thepredicate-argument structures resulting from their NLP systems withconstraints indicating that certain atomic formulas must occur withinthe scope of some formulation. In general, however, informationregarding quantification and scope is lacking.

The number of predicates that may occur in the results of NLP using alexicalized unification grammar can be on the order of the number oflexical entries in its vocabulary when the predicates are represented inDavidsonian manner. In a dependency-based parser, such as are common inNLP based on machine learning, the relationships in the results ofdependency-based parsing use a finite set of predicates the cardinalityof which is typically in the dozens. There is a rough correspondencebetween the predicates used in dependency-based grammars and aneo-Davidsonian representation of the predicate-argument structuresproduced by lexical unification grammars. Although the results ofdependency-based parsing do not include non-atomic formulations, per se,the limited information regarding non-atomic formulations and theirscope provided by lexical unification grammars can be obtained bycomputation given the dependency relationships of a parse and the partsof speech by which the arguments in those relationships are typicallytagged.

In the present invention, predicates of atomic formulas produced byunderlying NLP systems in Davidsonian or neo-Davidsonian are classifiedwithin an ontology of semantic types. If such an atomic formula wasintroduced by or refers to a head lemma rather than a rule in thegrammar, the lemma is associated with the atomic formula. If therepresentation produced by NLP lacks the Davidsonian variablerepresenting the state in which the atomic formula holds, such avariable may be introduced. In the case of any non-atomic formulasresulting from NLP, such as for conjunctions, negation, and modalformulations, each is typically also given Davidsonian variables. Insome cases, such as for nominal predicates, in particular, Davidsonianvariables may be omitted on the assumption that an entity will notchange types. If desired, however, the present invention may addDavidsonian variables in all cases.

In the event that an expected argument is omitted, such as in the caseof ellipsis, some lexical unification grammars will include variablesfor such elided arguments. In other cases, such as in the case ofdependency grammars, if information such as the lexical entries of theunderlying NLP system is available, additional variables may beintroduced for such elided variables.

For all variables referenced in the formulas resulting from NLP theremust be a quantifier introducing such variable in any logical axiomsrepresenting the meaning (logical semantics) of the sentence.Consequently, the present invention ensures that all such quantifyingformulations exist for the formulas of a given parse, particularly inthe case of an unambiguous sentence prior to logical disambiguation. Forexample, FIG. 7 shows completely under-specified quantification of theDavidsonian variables “?e2” and “?e27” and for the elided argument“?i26”.

The determination of precise logical quantification is beyond the stateof the art in natural language for various reasons. For example, in thesentence, “a cell has a nucleus”, there are various plausibleinterpretations for ‘a’ including universal and existentialquantification, as well as singular and plural quantification. Theintended semantics is that for all cells, of which there exist more thanone, there typically exists only one nucleus in the cell which is notwithin any other cell. This might be more clearly stated as “Almostevery cell has its own nucleus”, for example. Note that even “every”cannot be assumed as universal quantification since some cells have nonucleus.

The present invention may leave a quantifier completely under-specified,as in ⊆(?x6)cell(?x6)⊆(?x8)nucleus(?x8)have(?x6,?x8), or applyheuristics to suggest a reasonable default, such as universal forcertain lexical entries or existential for others. In doing this, thepresent invention incorporates an ontology of lexical entries whichintroduce quantification. For the most part these are articles,including determiners, but other logic, such as modification of nouns bycardinalities or modification of determiners by adverbs orpre-determiners are also considered. The essential point is that noheuristic is reliable. The safest heuristic is to assume a quantifier isexistential but even such a cautious heuristic is unreliable, such as inthe case of negation.

When presenting the logic for a selected parse or unambiguous sentenceas in FIG. 7, a preferred embodiment defaults to existentialquantification and provides a user interface by which the quantifier maybe changed, such as to universal quantification. For the above example,there would be two readings depending on which of the quantifiers iswithin the scope of the other, as in

∃(?x8)(nucleus(?x8)∧∃(?x6)(cell(?x6)∧have(?x6,?x8)))

versus

∃(?x6)(cell(?x6)∧∃(?x8)(nucleus(?x8)∧have(?x6,?x8))).

The present invention incorporates an ontology of quantification inwhich a quantifier is classified in several aspects. Quantification maybe singular or plural. For example, there may exist only one ofsomething, such as a mother, or there may exist more than one, such asfor ancestors. Complex quantification is commonly terse in naturallanguage, such as the negative universal, “not every” which is typicallyplural existential.

For example, the subscript ‘1’ in

∀(?x6)cell(?x6)⇒∃₁(?x8)(nucleus(?x8)∧have(?x6,?x8))

denotes singular existential quantification. Such rendering is logicallyequivalent to

∀(?x6)cell(?x6)⇒∃₁(?x8)(nucleus(?x8)∧have(?x6,?x8)) but is much simplerto read and more closely aligned with natural language text.

Similarly, the subscript ‘1’ in

∀(?x8)cell(?x8)⇒∀₁(?x6)nucleus(of)(?x6,?x8)⇒

(small)(?x6,?x8)

denotes singular universal quantification. Such rendering is logicallyequivalent to

∀(?x6)cell(?x6)⇒

(∃(?x8)(nucleus(of)(?x6,?x8)∧⇒¬(∃(?x9)(nucleus(of)(?x6,?x9)∧¬(?x8≡?x9)))

and

∀(?x6)cell(?x6))⇒∀(?x6)nucleus(of)(?x6,?x8)⇒

(small)(?x6,?x8)

where the first of these renderings is preferred in the presentinvention as more comprehensible.

The quantifier aspect ontology includes: existential, universal,negative, singular, and plural aspects and quantifiers composed fromthem. These quantifiers are presented in a drop-down selection list inthe logic column of FIG. 7 when selected by users.

Additional types of quantification supported include singularquantification over an individual, as in the use of a proper noun orpronoun, for example. Similarly, a noun may refer to a type or setrather than individuals which may introduce a higher than first-ordervariable or support distributive or collective reference, where thevariable introduced denotes a set and a reference to the variable may beto a member or subset, whether proper or the entirety of the set.

In addition to ambiguity with regard to the quantifier, the userinterface depicted in FIG. 7 addresses another need for disambiguationof the system of the present invention. The information provided by NLPsystems generally lacks information about the relative scope ofquantifiers. In some cases, such as using certain lexical unificationgrammars, an NLP system may indicate some constraints that an atomicformula must be within scope of another formula, but any suchinformation is far from complete or sufficient for logicaldisambiguation.

An important observation of natural language is that universalquantification expressed in English, for example, ubiquitously involvesan antecedent and a consequent. That is, universal quantificationexpressed in natural language practically requires each of an antecedentand consequential formula. Alternatively, universal quantification innatural language can be viewed as having scope over an implicativeformulation. Existential quantification, on the other hand, applies to asingle formula which may or may not be atomic.

In the present invention, when implicative quantification is specifiedduring disambiguation, such as in the case of selecting the universalquantifier, it must be the case that an atomic formula referencing thevariable it introduces occurs outside of negation as part of thequantifying formulations antecedent (or, in the alternative view, withinthe antecedent of its implicative formulation). Furthermore, thequantifying formulations that introduce any other variables in theformula that must be part of an antecedent must have scope over suchantecedent. Further, if a quantifying formulation is within the scope ofanother it cannot have scope over both of the broader formula'santecedent and consequent.

For example, when universal quantification is specified for thequantifier concerning the cell in the sentence, “a cell has a nucleus”,the present invention defaults to an implicative interpretation renderedas ∀(?x6)cell(?x6)⇒∃(?x8)(nucleus(?x8)∧have(?x6,?x8)). Furthermore, thepresent invention defaults to an existential implicative interpretationof universal quantification, which may be explicitly rendered as in:

∀∧∃(?x6)cell(?x6)⇒∃(?x8)(nucleus(?x8)∧have(?x6,?x8))

for example, where ‘∀∧∃’ is more comprehensible shorthand than renderingthe conjunction of:

∃(?x6)(cell(?x6)∧nucleus(?x8)∧have(?x6,?x8))

and

∀(?x6)cell(?x6)⇒∃(?x8)(nucleus(?x8)∧have(?x6,?x8))

The present invention also augments any constraints received from NLP toinclude the implications of the constraint that every atomic formulathat references a variable must be within the scope of the quantifyingformula that introduce said variable.

In addition to the above, linguistic conjunctions, whether implicit orexplicit, introduce additional constraints. The present inventionclassifies the semantic predicates produced by NLP for linguisticconjunctions as either subordinating or coordinating. All formulaswithin a coordinating formulation must reside within the scope of saidformulation. In the case of formulations produced for subordinatingconjunctions, the present invention classifies each as forward orreverse implicative, each of which requires both an antecedent andconsequential formula. For example, the present invention classifies oneof the lexical entries for ‘while’ as reverse implicative. Consequently,after disambiguation, the sentence “the plasma membrane of a cellexpands while it grows” may be rendered in one configuration of thepresent invention as

∀(?x8)cell(?x8)(∀(?x6)plasma(membrane)(of(?x8))(?x6)⇒expand(?x6)⇐grow(?x8))

where the Davidsonian variables for the verbal predicates have besuppressed for convenience.

Variables occurring in both the antecedent and consequential formulas ofsuch implicative formulations must be introduced by quantifyingformulations which have scope over all formulas in either (both) itsantecedent and consequent. Furthermore, the implicit antecedent formulamust reference the variable introduced by its quantification and do sooutside of negation so as to provide a binding for any other reference.Moreover, the consequential formula must also be within the scope of thequantification but outside the scope of the antecedent. Existentialquantification, on the other hand, has scope over a single formulawherein the variable must be referenced non-negatively so as to providea binding.

Note that enforcing the insights regarding the typically existential andimplicative nature of quantification in natural language also increasesdisambiguation productivity since such additional (implicit) constraintsreduce ambiguity.

All formulas arising within a noun phrase must be within the scope ofthe quantifying formulation introducing the variable for said phraseshead. Such a constraint also holds for other types of phrases andclauses.

The point here is not to enumerate the full logic of the presentinvention with regard to deriving constraints but to indicate that manyconstraints accumulate so as to constrain the relative scopes ofquantifying formulations arising from NLP. In the common case, however,all such constraints, no matter how completely considered and enforced,do not typically resolve all relative scope ambiguity.

A preferred embodiment of the system of the present invention provides auser interface, such as depicted in FIG. 7 and as illustrated in FIG.12, by which a user can add constraints on the relative scopes ofnon-atomic formulas in particular. In said user interface, a formula,such as a quantifying, negative, modal, or subordinating or coordinatingformulation, may be constrained to be within another such formulation bydragging the first said formulation and dropping it on the second.Alternatively, the user may specify using a drop-down selection in the“within” column depicted in FIG. 7 from which another non-atomic formulamay be selected. The user interface precludes dropping one formula onanother in violation of existing scope constraints or informs the userif doing so via the drop-down list would violate existing constraints.The user interface also allows user-specified constraints to be relaxedby either operation. Moreover, the user interface allows a formula to bespecified as having outermost scope contain all other formulas if such aspecification does not violate constraints other constraints, especiallythose not specified by users.

A preferred embodiment of the system of the present invention provides auser interface, such as depicted in FIG. 7 and as illustrated in FIG.12, by which a user can specify equalities or inequalities betweenvariables, such as in the case of definite reference, occurrences ofpronouns, and other forms of anaphora. In said user interface, thevariable of one quantification may be specified as being the same as ordisjoint from the variable of another variable. Variables specified asbeing the same are logically equal and do not require more than a singlequantification in any resulting reading or axiom. Variables that aredisjoint correspond to a negated equality and require distinctquantifying formulations.

In addition to presenting the lattice of scope constraints, such as inthe tree of non-atomic formulas shown in FIG. 7, the present inventioncomputes and presents the set of readings that are consistent with suchconstraints such as in the table of readings at the bottom of FIG. 7.Such computation and presentation occurs after grammaticaldisambiguation and after each logical disambiguation activity depictedin FIG. 12. As the user adjusts scope constraints the readings areupdated in real time. The user may select any of these readings and anyadditional scope constraints will be immediately reflected in the treerendering of such constraints.

FIG. 8 illustrates the effect of specifying an equality, clarifyingseveral quantifiers as universal, and selecting a reading from thosethat were consistent with such specifications. Note the singularuniversal quantification indicates an implication unlike existentialquantification, as discussed above. The depicted sentence has beenreduced to the point of a single axiom. Upon closing the tab for thissentence within the user interface, the XML document for the sentence isupdated in the knowledge base and propagated to other clients of theknowledge base as being axiomatic and last acted upon at the time ofconclusion by the user who disambiguated the sentence.

The table of readings in FIG. 7 shows only two readings for the selectedparse. This reflects conflation of formulas as discussed below andsuppression of variables that are “optional”, such as Davidsonianvariables and variables corresponding to ellipsis (i.e., elidedarguments) which are not co-referenced. Eliminating such variables fromformulas also allows the quantifying formulations that introduce them tobe omitted from readings, thereby reducing the number of readings byeliminating ambiguity with regard to relative scope. Typically, when thepresent invention drops an elided argument it typically uses a relatedpredicated. For example, in the case of transitive nouns or adjectivesused intransitively, predicates often include an ending preposition,such as “end(of)” as shown in FIG. 6. When the present inventionsuppresses an elided transitive argument from a formula the resultingformula uses the related transitive predicate, such as “end”. Similarlogic is brought to bear for verbal predicates that expect prepositionalcomplements, as discussed further below.

The present invention simplifies logical disambiguation and facilitatesoptimized reasoning performance when the logic resulting from suchdisambiguation is used with logic programming systems by composingpredicates and conflating atomic formulas as shown in FIG. 11. Thereadings of FIG. 7 demonstrate the effect of conflating compounds,modifiers, and complements using “phrasal style” and “existentialfunctors” as reflected in the selection of “all” in the combo box at theright of the toolbar in FIG. 7.

FIG. 9 shows that the number of readings for a parse increases from 2 to48 for the selected parse when no conflation is specified in thedrop-down box on the right of the toolbar and without suppression of“optional” arguments specified by the “ . . . ” toggle near the middleof the toolbar. FIG. 10 shows 16 parses for the selected sentences withsuppression of “optional” arguments but without conflation. Again, thiscontrasts with 2 when all conflation is in effect for the selectedparse.

The present invention applies a number of transformations to conflateformulas as shown in FIG. 11. In many cases, the conflation of formulasresolves arguments elided from other formulas. For example, if a parseexpects a prepositional complement that is not recognized by the NLPsystem, it may produce two formulas, one representing the link betweenthe head of a prepositional phrase with the variable it complements andanother for the clauses headed by the verb with an elided argument forits unrecognized but expected complement. In the event the predicate ofthe prepositional formula is compatible with the expected complement theprepositional two formulas can be conflated. The most common case forsuch conflation of formulas resolving elided arguments is to resolveomitted “of” complements, however.

Conflation resolves “of” complement arguments elided within formulaswhen the prepositional formula (whether for “of” or a possessiveconstruction) occurs within the same scope of quantification andnegation. For example, “a part attached to its assembly” will produce atransitive “part(of)” predicate with an elided argument (i.e., avariable that is not referenced in any other formula) and a “possessive”predicate with a un-referenced variable for the pronoun. For logicalreadings in which these two predicates are within the same scope ofquantification and negation, they may be conflated. In performing thisconflation, the predicate “part(of)(?part,?)” and“possessive(?its,?assembly)” become “part(of)” (?part,?assembly)” wherequestion marks indicate variables with or without a name. Note, however,that such conflation would be possible only after an equality between‘its” refers to the “part” such as using the co-reference capabilitiesdiscussed above. Also note that this example may be viewed assuppression plus more general conflation.

In the event that the elided variable in the example above issuppressed, as discussed above, “part(of)(?part,?)” becomes“part(part)”. This is equivalent to the result from parsing a word thatis not a partitive or transitive and allow it to be complemented, as istypically the case for nouns and verbs. In this case, conflation of acomplement proceeds with its more general definition. Conflation of acomplement involves the composition of a predicate and the addition ofan argument. Furthermore should not proceed arbitrarily but be done inaccordance with a methodology in order for resulting logic to beeffective. In the example of “part(?part)” and“possessive(?its,?assembly), the latter effectively becomes“possessive(?part,?assembly)” after resolving pronomial anaphora. In apreferred embodiment where conflation is performed using “phrasalstyle”, the result will be part(of(?assembly))(?part). In anotherpreferred embodiment, conflation results in “part(of)(?part,?assembly)”.Note that in each case, the possessive is treated as a special case of“of”.

Note that we have omitted Davidsonian arguments for simplicity in theprior and following paragraphs.

In a preferred embodiment, the methodology of conflation for complementsis to append complementing predicates to the predicates they use. Insuch a preferred embodiment, the complementing predicate should beappended in a manner that can be decomposed at runtime and ontologicalinformation about predicates resulting from such conflation may be madeavailable for use by logic programming technology. For example, in apreferred embodiment, the conflated predicate is defined within anontology identifying the conflated predicating and how it comprises thecomplemented and complementing predicates. Such information issubsequently made available as declarative statements.

For example, the following show the declaration of compositionalpredicates and related logic produced by a preferred embodiment of thepresent invention,

1. rdfs: subPropertyOf(assembly(of),of)

2. rdfs:domain(assembly(of),assembly)

3. forall(?x){circumflex over ( )}(assembly(?x)<==>exist(?y){circumflexover ( )}assembly(of)(?x,?y))

4. forall(?x,?y){circumflex over( )}(assembly(of)(?x,?y)<==>(assembly(?x) and of(?x,?y))

These axioms relate the unary nominal to its binary transitive form inweb ontology language and first order logic (using SILK syntax).

Conflation of modifiers, which typically (ignoring Davidsonianarguments) do not have arguments other than what they modify, is similaralthough in a preferred embodiment, the modifiers by default compose asa prefix “around” what they modify. For example, in “a red ball”, thepredicates “red(?ball)” and “ball(?ball)” compose into“red(ball)(?ball)”.

As in the case of conflation of complements, a preferred embodimentmaintains an ontology of information concerning predicates composedduring conflation and which is the complementing or modifying predicateas well as which is the complemented or modified predicate. Also, asdiscussed above in the case of complements, a preferred embodimentprovides information for external use, such as during reasoning by alogic program. For example, the following axioms might be provided:

1. composition(red(ball),ball,red);

2. red(ball)(?ball)<==>(red(?ball),ball(?ball));

The first of which is a declarative representation as a Prolog factsuitable for processing using HiLog axioms written in SILK. For example,the 2^(nd) axiom above is implied by the following HiLog axiom given thefirst, as in a preferred embodiment:

?modifies(?modified)<==>(?modifies(?x),?modified(?x))

:-

composition(?modifies(?modified),?modified,?modifies),

unary(?modified),unary(?modifies);

Where axiom 1 above corresponds to the head (preceding the “is impliedby” sign :-) and axiom 2 above unifies with the first atomic formula ofthe body and provided “red” and “ball” are unary predicates.

Note that some post-modifiers may be composed as in?modified(?modifies). More importantly, note that composition canproceed through multiple levels. For example, consider the followingpredicates from FIG. 8:

1. plasma(membrane)

2. lipid(of(?))

3. hydrophobic(end(of(?)))

4. be(orient)(away(from))

The occurrences of “(?)” indicate arguments of complements composedusing phrasal style. Such predicates are easier for people to read. Theyare easily supported by logic programming systems such as Prolog, forexample, with axioms like:

lipid(of)(?membrane,?lipid):-lipid(of(?membrane))(?lipid)

Note, importantly, that conflation must consider referenced Davidsonianvariables. If the Davidsonian variable of a modifier or complement isreference it cannot be conflated with what it modifies or complementssince doing so eliminates the Davidsonian variable as shown above. Inpractice this has little effect if the conflation process is defined toproceed in a bottom-up or opportunistic manner. For example, if themodifier or complement that reference a Davidsonian variable of itsmodified or complemented formula (as is typical case by which suchmodification or complementing is recognized) is conflated with saidmodified or complemented formula, it is frequently the case that noother reference to said modified or complemented formula then existssuch that the resulting formula, with a composed predicated, may nowproceed to be conflated with what it modifies or complements.

As an example of compound conflation, consider the sentence, “Mostsecretory proteins are glycoproteins, proteins that are covalentlybonded to carbohydrates.” Logic resulting for this sentence involves the“be(covalent(bond))(to(?))”. To obtain this result, the system conflatedthe “covalent” modifier with “be(bond)” and the result was conflatedwith a “to” complement. In the preceding, note the use of the adjectivallemma for the deadjectival adverb applied to the verb.

The predicate “be(bond)” of the prior paragraph demonstrates anotherform of conflation supported by the present invention. The NLP systemproduced a predicate with an unresolved subject using the verb “bond”and noted that it was used in passive voice. The present inventiongenerally refuses to suppress elided arguments when they are a coreargument of their predicate, as is the case for the subject of a verb.In passive constructions, however, the subject argument is frequentlyelided. Generally, however, suppressing elided arguments is limited todropping final elided arguments (unless there is more complexinformation concerning a related predicate having the same argumentsexcept for the elided argument). In the case of passive constructionswith elided arguments, the convention used in the present invention isto compose the predicate along the following lines:

1.be(?infinitive)(?object)<==>(?infinitive(?subject,?object,?complement)

-   -   :-    -   passive(be(?infinitive),?infinitive), SVOC(?infinitive)

Where, again, Davidsonian variables are omitted for simplicity and SVOCdenotes the typical pattern of a verb of valence 3 consistent of asubject, object, and complement, in that order, when used in activevoice.

Returning to the composition of “be(covalent(bond))(to)” from above, thecompositional machinery considers that “be(bond)” is an argument patterncomposition rather than a predicate resulting from modification orcomplementing. Thus, the conflationary algorithm penetrates the“be(bond)” to perform modification on the active form.

An important special case of conflation of modifiers concerns compoundwords. In the case of “plasma membrane” in the sentence of FIG. 10, theNLP system produces a quantifier for each of “plasma” and “membrane”.Unlike most modifiers, this yields an extra variable to be carriedthrough modification, as in producing a predicate“plasma(?p)(membrane(?m))” instead of “plasma(membrane)(?m)”. In mostcases of compounds, however, the variable of the modifier lacksco-reference. In the present invention, if conflation of compounds ispermitted, as in the case of FIG. 6 above, compounds with non-headarguments that lack co-reference are treated as modifiers without thenon-head argument. Note the impact that conflation of compounds has onreadability (i.e., ease of use and disambiguation productivity):

-   -   ∀(?x6)∃(?x8)(channel(?x8)∧compound(?x6,?x8)∧protein(?x6))⇒protein(?x6)∧transmembrane(?x6)    -   ∀(?x15)channel(protein)(?x15)⇒transmembrane(protein)(?x15)        Also note the elimination of a quantifier which simplifies scope        disambiguation and reduces the number of readings.

The part modifying the head of a compound stands in some underlyinglogical relation to the head. For example, a “storage disease” is a“disease affecting storage”, a “channel protein” is a “protein with achannel”, and a “cell membrane” is a “membrane [that is part] of acell”. In the present invention, compounds trigger additional knowledgeacquisition activity. Even without additional knowledge about therelationship underlying a compound, however, the present inventionsupports the inference that satisfying a compound implies satisfying thehead predicate, as in the following SILK:

?head(?x)<==?modifier(?head)(?x)

:-

compound(predicate)(?modifier(?head));

Which corresponds to Prolog including the following:

1. membrane(?m):-plasma(membrane)(?m);

2. compound(predicate(plasma(membrane));

As introduced above, existential functors are another kind ofconflation. Unlike other forms of conflation, in which two formulasbecome one, frequently eliminating a quantifier, existential functorsare terms conflating a quantifying formula with the only atomic formulathat references the variable said quantification introduces. The resultof such conflation is a term which may be considered as a skolem intraditional logic programming.

For example, the following shows the effect of conflation for thesentence, “All organisms have cell membranes”.

-   -   ∀(?x6)organism(?x6)⇒∃(?x8)(∃(?x13)(cell(?x13)∧compound(?x8,?x13)∧membrane(?x8))∧have(?x6,?x8))    -   ∀(?x6)organism(?x6)⇒have(?x6,∃(cell(membrane)))

Reading this sentence is significantly easier because there is no needto trace a variable to a quantifier and another atomic formula. Thelogical effect of such conflation is to bring the quantification of avariable to its smallest scope. Although such conflation may not havethe intended semantics, it has proven useful and productive duringdisambiguation. In the example above, the existential is appropriatelynarrow. If the intended semantics was to say that there exists anindividual cell membrane that is common to all organism, however, thescope of the existential should include the universal. Consequently, thepresent invention allows conflation of quantifiers to be toggled on andoff, so as to verify or clarify all scopes finally. In practice cases,however, the intended semantics is typically for existential scope to benarrow with respect to universal quantification and the order of nestedof existential quantification has no effect on logical semantics unlessa non-existential non-atomic formula intervenes.

In its most aggressive conflationary configuration, the presentinvention may also conflate universal quantifiers as for existentialfunctors discussed above. That is, if the variable for a universalquantification is referenced only once, that reference can be renderedas a term. For example, after conflating the existential above, theresulting universal quantification is referenced only once and may beconflated as follows:

∀(?x6)organism(?x6)⇒have(?x6,∃(cell(membrane)))

have(∀(organism),∃(cell(membrane)))

Note the result includes no variables. This corresponds to sentence ofdescription logic, such as:

-   -   rdfs:subClassOf(organism,owl:someValuesFrom(have,cell(membrane)))

That is, the quantifiers of description logic are implicit so as topreclude explicit variable references. By resolving quantifiers usingequality and conflating atomic formulas where possible and allowed, thepresent invention effectively translates many sentences of naturallanguage into description logic.

Transitive verbs are frequently used in passive voice and ergative verbsused intransitively replace their semantic subject with their directobject. In such cases, the subject is frequently omitted from thesentence. The present invention automatically defines passive predicateswithin its lexical ontology and supports reasoning with them versustheir transitive counterparts.

For example, consider the sentence, “Proteins are embedded in thephospholipid bilayer.” There is no syntactic subject or semantic agentof the verb “embedded” in this sentence. Consequently, NLP producesparses in which proteins constitute an argument of an underlyingpredicate which is missing its subject argument. In addition, NLPtypically produces an additional predicate indicate the passiveconstruction. In this case, NLP also produces a predicate indicatingthat the prepositional phrase using “in” is complements the predicatefor “embed”.

Rather than producing a quantifier for the missing subject, the presentinvention conflates the passive indicating predicate with the verbalpredicate when the subject is omitted in such cases, as shown below:

-   -   ∀(?x10)∃(?x16)(phospholipid(?x16)∧compound(?x10,?x16)∧bilayer(?x10))⇒∃(?e2)∃(?x5)(protein(?x5)∧in(?e2,?x10)∧be(embed)(?e2)(?x5))

In addition, the present invention typically conflates the prepositionalcomplement with the verbal predicate and also conflating the compound,as discussed above, yielding in this case:

-   -   ∀(?x10)phospholipid(bilayer)(?x10)⇒∃(?x5)(protein(?x5)∧be(embed)(in(?x10))(?x5))        And, if permitted to conflate quantifiers that introduce        variables referenced only once, further conflates this into:    -   be(embed)(in(∀(phospholipid(bilayer))))(∃(protein))        which corresponds to a sentence of description logic, such as        the following which may be generated by the present invention        depending on the target reasoning system (e.g., for an OWL-DL        reasoner):    -   rdfs:subClassOf(phospholipid(bilayer),owl:someValuesFrom(be(embed)(in),protein))        The present invention also supports reasoning with such        ontological representation by generating supporting axioms, such        as:    -   embed(in)(?e)(?y,?z,?x)<==>(embed(?e)(?y,?z),in(?e)(?z,?x)))        Where the variable “e” is a Davidsonian variable which may be        omitted, as in:    -   embed(in)(?y,?z,?x)<==>(embed(?y,?z),in(?z,?x)))

While each of suppression and conflation reduce the number of readingssignificantly, in combination their effect can be dramatic. In bothcases the reduction in the number of readings results from a eliminationof quantifying formulations and ambiguities of the scope relative toother quantifiers. Conflation is particularly effective in reducing thecomplexity of individual readings as reflected in the relative widths ofthe horizontal scroll bars in FIG. 9 versus FIG. 7.

As another example, the following demonstrates the effect of variousforms of such conflation for the interrogative sentence “If a Parameciumswims from a hypotonic environment to an isotonic environment, will itscontractile vacuole become more active?”.

-   -   ∀(?x13)environment(?x13)∧hypotonic(?x13)⇒∀(?x20)environment(?x20)∧isotonic(?x20)⇒∀(?x9)paramecium(?x9)⇒∀₁(?x29)possessive(?x29,?x9)∧contractile(?x29)∧vacuole(?x29)⇒if(then)(become(identical)(?x29,∃(?e43)more(?e43)∧active(?e43)(?x29)),        ∀(?e12)from(?e12,?x13)∧to(?e12,?x20)∧swim(?e12)(?x9))    -   ∀(?x9)paramecium(?x9)⇒∀₁(?x29)contractile(vacuole)(of(?x9))(?x29)⇒if(then)(become(identical)(?x29,more(active)(?x29)),        swim(from(∀(hypotonic(environment))))(to(∀(isotonic(environment))))(?x9))

The number of atomic formulas is reduced by more than half and thenumber of variables with scope to be resolved is reduced by two thirdswhile the number of readings falls from several hundred to less thanten.

The present invention improves upon the disambiguation of semantics bydiscriminating among formulas rather than being limited to finer-grainstructure. Discriminants include tuples corresponding to predicates andtheir arguments rather than just discriminants per argument. Such tuplesare illustrated in FIGS. 5 and 14, for example. The net effect is tomake choices easier to comprehend for most users and to reduce incorrectdisambiguation steps. Simply put, presenting formulas provides morecontext and reduces technical linguist details. It also anticipateslogical disambiguation by providing a more uniform perspective thanfiner-grain perspectives.

The present invention presents different views of discriminants. In FIG.14, for example, the default configuration of focusing on semantictuples is reflected in the combo-box next to the rightmost on thetoolbar. Users may examine all discriminants that are consistent withthe parse in focus, such as by selecting a parse as in the uppermosttable of FIG. 7. Alternatively, if a sentence is grammaticallyunambiguous, such as indicated by the checked and only parse in the sametable shown in FIG. 8, the tab labelled “clauses” will contain all thediscriminants that are consistent with the parse and indicate which havebeen explicitly affirmed by disambiguators. In addition, the “clauses”tab will also indicate any discriminants that have been refuted bydisambiguators.

Other views of discriminants focus on lexical or syntactic ambiguities.Focusing on lexical ambiguities requires the least skill and may be donequickly in a workflow between collaborators using the real-timepropagation of work discussed above and reflected in FIGS. 2 and 13. Asdiscussed above, the syntactic perspective may be focused in a bottom-upmanner, such as by affirming constituent noun phrases before presentingdiscriminants that depend on them. As with lexical disambiguation, lessskill is required than for semantic disambiguation and workflow betweendisambiguators of differing skill, interest, or focus is supported bythe architecture of FIG. 2 and the workflows depicted in FIGS. 2 and 3.In any case, disambiguation using the default focus on semantics hasproven to be easily and quickly mastered without requiring significantlinguistic or logical expertise. And in each case, focusing on a subsetof discriminants reduces cognitive load and increases productivityversus presenting all discriminants. For example, in FIG. 14, thecaption of the clauses tab indicates that of 111 discriminants, 55 ofwhich participate in all 75 parses shown in the sentences tab, only 19are presented when focusing on semantics. It is rare that more than ahandful of selections are required to fully disambiguate even moderatelylong sentences, such as depicted in the Figure.

Note that large numbers of syntactic discriminants are possible. Asdiscussed above, the present invention suppresses syntacticconstructions that are peculiar to an underlying NLP system.Nonetheless, as first discussed above, there may be many syntacticparses for a sentence or partial spans of its text. Aside from thelexical ambiguity of tokens and their parts of speech, ambiguities inthe attachment or inclusion of phrases or clauses with respect to eachother as well as ambiguity in the scope of linguistic connectives resultin syntactic ambiguity that is combinatoric with respect to the numberof lexemes in a sentences. Syntactic ambiguity is further compounded bylexical ambiguities.

The present invention reduces the impact of lexical ambiguity as part ofdetermining syntactic discriminants. The noun phrase “the concentration”given lexical entries for mental and chemical senses of concentrationwill produce two distinct noun phrases. In the present invention, onlyone discriminant, “NP(the(concentration))” would be presented, avoidingan explosion of distinct discriminants that depend on two that would beotherwise presented. As the nesting of modifiers, complements, phrases,and clauses increases the combinatorics are significant. And avoidingsuch combinatorics is not as simple as presenting coarsely labeled parsetrees. The method for constructing syntactic discriminants intentionallyavoid the variety of non-terminals that may exist in various NLPsystems, including derivational nodes as may exist between the verb“concentrate” and the noun “concentration” in even this simple case.

The present invention improves syntactic disambiguation using bottom-updisambiguation which further extends its utility to large vocabulary,broad coverage NLP systems. The present invention suppresses longer andmore linguistically sophisticated types of phrases and clauses, at leastuntil shorter, simpler choices have advanced disambiguation. Forexample, phrases or clauses that have a noun phrase as one of theirconstituents may be suppressed from presentation until the constituentnoun phrase is affirmed through disambiguation.

The present invention allows choices that appear to contradict theresults of NLP. For example, if all parses considered duringdisambiguation imply a given choice, prior systems suppress (i.e., donot present) such choices since in such systems they would not furtherdisambiguation. Such systems are based on the premise that exhaustiveparsing has taken place off-line while the present system may iteratewith NLP for various reasons described above. This assumption isreflected in the more basic and limited algorithms used in suchapproaches to discrimination. For example, assuming that there are noplausible parses beyond those presently under consideration allows forpositive inferences that may be unfounded, such as considering adiscriminant inconsistent if it represents a phrase that does not occuramong the presently considered parses. Although the present inventionmay operate under such an assumption of completeness, it is not limitedto doing so. In particular, the present invention can discriminate evenwhen there are no full parses or, more importantly, when the number ofplausible parses may be astronomical, such as in the case of statisticalparsers.

The present invention may also present discriminants that are implied bypreviously specified discriminants rather than suppress them since theywould not further discrimination under the exhaustive prior off-line NLPassumption mentioned above. In the event that such an implieddiscriminant is refuted, the present invention repeats NLP withoutrequire prior exhaustive parsing. In the event that exhaustive parsingproduces no consistent parse, the presentation of prior affirmations andrefutations may be relaxed and NLP resumed without such constraints. Thesystem is not limited to restarting although, for practical reasonshaving to do with client/server architecture, that is its default modeof operation. In any case, the constraints providing duringdisambiguation inform and constrain NLP, allowing it to be perform in amore focused, efficient, and iterative manner. For example, ifdisambiguation rules out any morphological tokens or narrows the partsof speech for any lexemes, which is ubiquitously the case, suchconstraints are conferred to the NLP system such that it does notconsider other possibilities, which reduces the combinatorics of parsingleading to improved focus and efficiency as well as improveddisambiguation productivity for longer sentences which are common inuncontrolled, natural language text.

The present invention is specifically targeted to obtaining the logicalsemantics of input text. As such, the present invention is not limitedto interpreting text as it exists in documents but with regard to theknowledge it intends to communicate. Unlike treebanking software, whichaims to annotate or disambiguate text as it exists without modification,the present invention deliberately supports the reformulation of text soas to facilitate knowledge acquisition (KA). Consequently, duringdisambiguation, the present invention explicitly supports revisions tothe input text that facilitate improved NLP, such as to overcomelimitations of an underlying NLP system's grammar or lexicon or toimprove the repository of sentences from the perspective of knowledgemanagement.

At any time, the input text of a sentence undergoing disambiguation maybe edited and reparsed while preserving annotations already clarifiedfor unedited parts of the text. This is particularly important andproductive with regard to sentences that contain superfluous phrases orclauses (e.g., introductory adverbials), parentheticals and rhetoricalflourishes (e.g., metaphors) that have little or no bearing on thelogical semantics to be captured from such content.

A preferred embodiment of the system of the present invention isspecifically directed to defeasible reasoning, which include reasoningunder uncertainty or default reasoning. The system provides forquantification beyond existential and universal quantification as inclassical logic and facilitates abductive reasoning. For example, in thesentence “bird fly”, a classical logic system would fail when also givenan axiom corresponding to “penguins are birds that do not fly”.Classical logic fails because of the inconsistent of a penguin beingdeduced as flying by one axiom and as not flying by the other. In adefeasible logic, such as Vulcan's SILK, neither conclusion would bereached precisely because of such a conflict. Despite the frailties ofclassical logic, prior experiments with translating natural languageinto logic have not discerned or grappled with the need fordefeasibility but have rather focused on probabilistic reasoning withless expressive logical formalisms than predicate calculus.

The present invention may operate in either of two modes with regard todefeasibility. The system may default to defeasible interpretation ofall quantifiers and/or be explicit with regard to defeasibility of asentence of quantifier, whether specified through the user interface orassumed by default when quantifiers are introduced by certain words. Forexample, in the sentence “most cells have a nucleus”, the universalquantifier may be assumed in most cases but allows for exceptions as aresupported in the reasoning of defeasible or default logic systems. Inthe case of this sentence, the explicit defeasibility of the quantifiermay be rendered using a superscript or by prefixing the quantifier witha sign, as in:

≲∀(?x6)cell(?x6)⇒∃(?x8)(nucleus(?x8)∧have(?x6,?x8))

A preferred embodiment of the present invention translates naturallanguage sentences into logical axioms that are consistent with theresults of NLP, such as readings depicted in FIGS. 7, 9 and 10, but moresignificantly, axioms resulting from logical disambiguation as in FIG.8, into first-order syntax for use in deductive inference. The presentsystem generates such logic in SILK syntax, for example, withtranslation between any two varieties of first-order syntax beingstraightforward and obvious to those of ordinary skill in the art.

A more preferred embodiment of the present invention translates in sucha manner with annotations added as required by a defeasible or defaultlogic system such as SILK. Such annotations specify whether such axiomsare (i) strict as in classical logic, and therefore brittle in the eventof inconsistency, or (ii) defeasible, as in default logic, such thatinconsistencies shall not cause logical failure or brittleness.

For example, the following shows a “tag” annotation which indicates theaxiom is defeasible (not strict) in SILK:

@[tag−>‘Every fluid is a substance.’,parse−>‘every’(‘fluid’)(‘is'(‘a’(‘substance’))), words−>5, parses−>1,readings−>1, warnings−>0, creator−>“pvhaley”, created−>“2013/03/1918:42”] forall(?x6)^({circumflex over ( )})(fluid(?x6) ==>substance(?x6));

A more preferred embodiment of the present invention further translatesin such a manner with annotations added to non-strict axioms so as toassist resolving inconsistencies arising during the inference performedby a defeasible or default logic system. Such annotations assistingresolution of inconsistencies include annotations with regard topriority or probability of axioms so annotated as may be supported byvarious target abductive, uncertain, default, or defeasible logicprogramming systems.

A more preferred embodiment of the present invention further soannotates axioms with information that may be used in meta-reasoning soas facilitate other logic, such as written in the target system, toascertain the priority or probabilities or conditioned on theinformation provided in such annotation and axiom, whether by inductionor by conflict resolution policies specified in the target system, as istraditional in defeasible or default logic programming systems whichincorporate argumentation as in SILK or Courteous Logic.

For example, in addition to defeasible axiom shown above, a preferredembodiment of the present invention also outputs the following strictaxiom which constitutes an annotation of the defeasible axiom asreflected in the shared attributes demarcated by the “->” symbols ineach:

@[strict, id−>‘Every fluid is a substance.’,parse−>‘every’(‘fluid’)(‘is'(‘a’(‘substance’))), words−>5, parses−>1,readings−>1, warnings−>0, creator−>“pvhaley”, created−>“2013/03/1918:42”] interpretation( ‘Every fluid is a substance.’, syntax(‘every’(‘fluid’)(‘is'(‘a’(‘substance’))),?_DET40(?_N46)(?_V66(?_DET79(?_N100))), [S(?_S377), NP(?_NP376),DET(?_DET40), N(?_N144), N(?_N46), VP(?_VP239), V(?_V66), NP(?_NP155),DET(?_DET79), N(?_N102), N(?_N101), N(?_N100)], [S(?_NP376(?_VP239)),NP(?_DET40(?_N144)), DET(?_DET40), N(?_N46), N(?_N46),VP(?_V66(?_NP155)), V(?_V66), NP(?_DET79(?_N102)), DET(?_DET79),N(?_N101), N(?_N100), N(?_N100)], [?_S377, ?_NP376, ?_DET40, ?_N144,?_N46, ?_VP239, ?_V66, ?_NP155, ?_DET79, ?_N102, ?_N101, ?_N100], [_377,_376, _40, _144, _46, _239, _66, _155, _79, _102, _101, _100] ),semantics( bindings( [?_x6, ?_x2, ?_x8], [_(‘fluid’), _(‘is'),_(‘substance’)], [?_x6, ?_x2, ?_x6], [_(‘fluid’), _(‘is'), ?_x6] ),[[_3, quantifier(every)(?_x6, _5, _4), ‘every’], [_7,nominal(fluid)(?_x6),‘fluid’], [_1, verbal(be(identical))(?_x2, ?_x6,?_x8), ‘is'], [_9, quantifier(a)(?_x8, _10, _11), ‘a’],[_12,nominal(substance)(?_x8), ‘substance’] ], [declarative(?_x2),indicative(mood)(?_x2), literal(?_x2), non(perfective)(?_x2),non(progressive)(?_x2), present(tense)(?_x2), referent(?_x6),referent(?_x8), singular(?_x6), singular(?_x8), third(person)(?_x6),third(person)(?_x8), universal(?_x6) ], [main(formula)(_1),main(variable)(?_x2), formula(within(hole))(_7, _5),formula(within(hole))(_12, _10), sameAs(?_x8, ?_x6) ], readings([universal(?_x6)([fluid(?_x6)]([universal(?_x6)([substance(?_x6)])])] )) );

Such annotations allow for policies such as length-, author- ortenure-based priority, for example, which are commonly used in conflictresolution.

In an example shown above, the axiom forall(?x6){circumflex over( )}(fluid(?x6)==>substance(?x6)); is a reading rendered in afirst-order logic syntax. This axiom is further translated into a logicprogramming syntax which is further translated into Prolog syntax byFlora-2 for execution using the XSB system. This capability has beenintegrated within an embodiment of the present invention such thatfirst-order or logic programming syntax may be output into filesdirectly. In the course of translating first-order SILK into Flora-2,the SILK system also generates intermediate axioms which can bestraightforwardly rendered as production rules for use in rule-basedsystems.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word “comprising” or “including”does not exclude the presence of elements or steps other than thoselisted in a claim. In a device claim enumerating several means, severalof these means may be embodied by one and the same item of hardware. Theword “a” or “an” preceding an element does not exclude the presence of aplurality of such elements. In any device claim enumerating severalmeans, several of these means may be embodied by one and the same itemof hardware. The mere fact that certain elements are recited in mutuallydifferent dependent claims does not indicate that these elements cannotbe used in combination.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred embodiments, it is to be understood that suchdetail is solely for that purpose and that the invention is not limitedto the disclosed embodiments, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

What is claimed is:
 1. A method for reducing the logical ambiguity of aportion of natural language text, the method comprising: a.algorithmically interpreting the portion of natural language text by acomputer system into a number of data structures representing aplurality of interpretations of said portion of natural language text asa number of logical formulas such that said plurality of interpretationsrepresent a number of ambiguities, wherein said data structures include:i. a number of first data structures representing formulas pertaining toor between referents within any of said plurality of interpretations,wherein each of the first data structures represents a predicate usingas an argument a variable corresponding to a thing or a situationreferenced explicitly or implicitly within said plurality ofinterpretations; ii. a number of second data structures representingformulas of logical quantification each pertaining to a variable used asan argument in a number of the logical formulas and wherein each logicalformula represented by a second data structure has or should have scopeover all other logical formulas which use the variable to which thelogical formula represented by the second data structure pertains; iii.a number of third data structures each representing a constraint on therelative scopes of a respective pair of formulas, wherein each of thethird data structures indicates whether one formula of the respectivepair of the formulas occurs within the scope of the other formula of therespective pair of the formulas; and b. in a user interface, enablingspecifying whether a first particular one of the logical formulas occurswithin a scope of a second particular one of the logical formulas so asto augment the constraints represented by the third data structures andreduce the logical ambiguity among the interpretations of said portionof natural language text.
 2. The method of claim 1, further comprising:c. algorithmically computing a set of readings of the logical formulasrepresented by said data structures, wherein each of the readings isconsistent with the constraints represented by the third data structuresand wherein each of the readings contains no logical ambiguity withregard to scope; and d. in a user interface, presenting the readingsresulting from the computing.
 3. The method of claim 2, furthercomprising: e. in a user interface, enabling selection of one of thereadings which is consistent with an interpretation of said portion ofnatural language text, where upon the set of constraints that hold forthe one of the readings augment the third data structures so as toreduce the logical ambiguity of said interpretation with regard toscope.
 4. The method of claim 1, wherein said data structures furtherinclude a number of data structures representing formulas of logicalnegation each of which has or should have scope over one other logicalformula.
 5. The method of claim 1, wherein said data structures furtherinclude a number of data structures representing formulas of logicalconjunction each of which has or should have scope over at least oneother logical formula.
 6. The method of claim 1, wherein said datastructures further include a number of data structures representingformulas of logical disjunction each of which has or should have scopeover at least one other logical formula.
 7. The method of claim 1, eachsecond data structure also representing whether or not the logicalquantification it represents should be interpreted as existential oruniversal in nature; and the method further comprising, in a userinterface, enabling specifying whether the second data structurerepresents a logical quantification that should be interpreted asexistential or universal in nature so as to reduce the logical ambiguityamong the interpretations of said portion of natural language text. 8.The method of claim 7, further comprising algorithmically computing aset of readings of the logical formulas represented by said datastructures, wherein each of the readings is consistent with theconstraints represented by the third data structures and wherein each ofthe readings contains no logical ambiguity with regard to scope, and, ina user interface, presenting the readings resulting from such thecomputing.
 9. The method of claim 1, wherein a logical quantificationmay be specified as being both existential and universal in nature. 10.The method of claim 8, wherein the user interface renders theexistential and universal logical quantification without also renderingan implied existential formula associated therewith.
 11. The method ofclaim 7, wherein a logical quantification may be specified as beingsingular such that the negation of an otherwise equivalent existentialquantification holds for any binding of its variable that is not equalto the binding of its variable for which said quantification holds. 12.The method of claim 11, wherein the user interface renders the singularlogical quantification without also rendering the implied negation ofthe otherwise equivalent existential quantification for a non-equalvariable.
 13. The method of claim 7, further comprising allowing whetherquantification is defeasible to be specified.
 14. The method of claim 1,further comprising allowing two of the variables to be specified asconcerning the same variable such that only one formula ofquantification is required and such that its variable substitutes forany occurrence of the variable of the quantification not retained. 15.The method of claim 1, further comprising allowing two of the variablesto be specified as distinct such that a statement of logical inequalitybetween the distinct variables is implied.
 16. The method of claim 1,wherein said data structures further include a number of data structuresrepresenting formulas of logical negation, conjunction, disjunction, orimplication, each having scope over at least one other logical formula.17. The method of claim 1, further comprising persistently representinga number of specifications of relative scopes.
 18. The method of claim7, further comprising persistently representing a number ofspecifications of relative scopes.
 19. The method of claim 7, furthercomprising outputting a number of readings for use with a first-orderlogic reasoning system.
 20. The method of claim 7, further comprisingoutputting a number of readings for use with a logic programming system.21. The method of claim 7, further comprising outputting a number ofreadings for use with a rule-based system.